System and method for automatic content aggregation evaluation

ABSTRACT

Systems and methods for content aggregation creation are disclosed herein. The system can include memory having a content database and an aggregation database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include a server that can: provide content to the user device via a first electrical signal; receive a selection of a portion of the provided content from the user device via a second electrical signal; automatically extract sentences from the selected portion of the provided content via a natural language processor; automatically generate a parse tree for one of the automatically extracted sentences; identify noun phrases from the part of speech tags within the parse tree; place content associated with one of the noun phrase in a content aggregation; and output the content aggregation to the user device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/377,863, filed on Dec. 13, 2013, and entitled “SYSTEM AND METHOD FORAUTOMATIC CONTENT AGGREGATION EVALUATION”, which is a continuation ofU.S. application Ser. No. 15/236,275, filed on Aug. 12, 2016, andentitled “SYSTEM AND METHOD FOR AUTOMATIC CONTENT AGGREGATIONGENERATION”, now U.S. Pat. No. 10,325,215, which claims the benefit ofU.S. Provisional Application No. 62/320,213, filed on Apr. 8, 2016, andentitled “ADAPTIVE PATHWAYS AND COGNITIVE TUTORING”, the entirety ofeach of which is hereby incorporated by reference herein.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, i.e., the communications protocols to organize network traffic,the network's size, topology and organizational intent. In most cases,communications protocols are layered on other more specific or moregeneral communications protocols, except for the physical layer thatdirectly deals with the transmission media.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for contentaggregation creation. The system can include memory including: a contentdatabase including content relevant to a common subject; and anaggregation database. In some embodiments, the aggregation database canstore one or several content aggregations. The system can include a userdevice having: a first network interface that can exchange data via acommunication network; and a first I/O subsystem that can convertelectrical signals to user interpretable outputs via a user interface.The system can include a processor that can: provide content to the userdevice via a first electrical signal communicated via the communicationnetwork; receive a selection of a portion of the provided content fromthe user device via a second electrical signal communicated via thecommunication network; automatically extract sentences from the selectedportion of the provided content via a natural language processor;automatically generate a parse tree for one of the automaticallyextracted sentences, which plurality of branches of the parse tree areeach associated with a part of speech tag; identify noun phrases fromthe part of speech tags within the parse tree; place content associatedwith one of the noun phrase in a content aggregation; and output thecontent aggregation to the user device.

In some embodiments, the server can identify a subset of the nounphrases, wherein the subset of the noun phrases comprises rationalphrases. In some embodiments, the rational phrases are identified basedat least on one of: size; or the presence of a trigger attribute. Insome embodiments, identifying the rational phrases based on sizeincludes: automatically determining the number of words in the nounphrase; retrieving a size threshold; comparing the number of words inthe noun phrase to the size threshold; and associating a first Booleanvalue with the noun phrase when the number of words is less than thesize threshold and associating a second Boolean value with the nounphrase when the number of words is greater than the noun threshold.

In some embodiments, the trigger attribute can be a number. In someembodiments, the processor can further store the content aggregation inthe aggregation database. In some embodiments, content is placed in acontent aggregation for each of the rational phrases. In someembodiments, identifying noun phrases from the part of speech tagswithin the parse tree includes: identifying the root of the parse tree;progressing from the root to a one of the plurality of branches of theparse tree; retrieving the part of speech tag associated with the one ofthe plurality of branches of the parse tree, and identifying the part ofspeech tag associated with the one of the plurality of branches of theparse tree. In some embodiments, identifying noun phrases from the partof speech tags within the parse tree further comprises repeating steps(a)-(d) for each of the plurality of branches of the parse tree.

In some embodiments, placing content associated with one of the nounphrase in a content aggregation includes: designating at least one wordof the noun phrase as an extraction; generating a presentation portionof the noun phrase by removing the extraction from the noun phrase;storing the presentation portion of the noun phrase in the aggregationdatabase; and storing the extraction in the aggregation database. Insome embodiments, the extraction is associated with the presentationportion.

In some embodiments, the at least one word is designated as theextraction via a random number generator. In some embodiments,designating the at least one of the noun phrase as an extractionincludes: identifying a part of speech for each of the words in the nounphrase; retrieving an extraction parameter, which extraction parameteridentifies at least one part of speech for potential identification asthe extraction; identifying the words in the noun phrase for potentialidentification as the extraction according to the extraction parameter;and designating the extraction from the words in the noun phraseidentified for potential identification as the extraction according tothe extraction parameter. In some embodiments, the processor canassociate a tag characterizing the content aggregation with a portion ofthe content aggregation.

One aspect of the present disclosure relates to a method for creation ofa content aggregation. The method includes: providing content to a userdevice via a first electrical signal communicated via a communicationnetwork; receiving a selection of a portion of the provided content fromthe user device via a second electrical signal communicated via thecommunication network; automatically extracting sentences from theselected portion of the provided content via Natural LanguageProcessing; automatically generating a parse tree for one of theautomatically extracted sentences; identifying noun phrases from thepart of speech tags within the parse tree; placing content associatedwith one of the noun phrase in a content aggregation; and outputting thecontent aggregation to the user device via a third electrical signalcommunicated via the communication network. In some embodiments, each ofthe plurality of branches of the parse tree is associated with a part ofspeech tag.

In some embodiments, the content is provided to the user device from acontent database comprising content relevant to a common subject. Insome embodiments, the user device comprises: a first network interfacethat can exchange data via a communication network; and a first I/Osubsystem that can convert electrical signals to user interpretableoutputs via a user interface. In some embodiments, the method includesidentifying a subset of the noun phrases. In some embodiments, thesubset of the noun phrases includes rational phrases. In someembodiments, identifying the rational phrases based on size includes:automatically determining the number of words in the noun phrase;retrieving a size threshold; comparing the number of words in the nounphrase to the size threshold; and associating a first Boolean value withthe noun phrase when the number of words is less than the size thresholdand associating a second Boolean value with the noun phrase when thenumber of words is greater than the noun threshold.

In some embodiments, identifying noun phrases from the part of speechtags within the parse tree includes: identifying the root of the parsetree; progressing from the root to a one of the plurality of branches ofthe parse tree; retrieving the part of speech tag associated with theone of the plurality of branches of the parse tree, and identifying thepart of speech tag associated with the one of the plurality of branchesof the parse tree. In some embodiments, placing content associated withone of the noun phrase in a content aggregation includes: designating atleast one word of the noun phrase as an extraction; generating apresentation portion of the noun phrase by removing the extraction fromthe noun phrase; storing the presentation portion of the noun phrase inthe aggregation database; and storing the extraction in the aggregationdatabase, wherein the extraction is associated with the presentationportion.

One aspect of the present disclosure relates to a method for evaluationof content aggregation. The method includes: retrieving a contentaggregation from a content library database, which content aggregationincludes a presentation portion and an extraction portion; selecting thecontent aggregation based on a content aggregation score generatedaccording to one or several identified attributes of the contentaggregation; extracting content features from the content aggregation,which content features include at least one count of at least one wordtype; retrieving a model, such as a statistical model, from a modeldatabase; and generating an evaluation result by inputting the contentfeatures into the statistical model, which evaluation result is theoutput of the statistical model.

One aspect of the present disclosure relates to a method for evaluationof content aggregation. The method includes: receiving a user identifierfrom a user device at one or more servers; identifying the user with theone or more servers; receiving an evaluation request, which evaluationrequest defines a set of content aggregations; retrieving the set ofcontent aggregations; retrieving object network data relevant to thedefined set of content aggregations, which object network dataidentifies a plurality of topics; comparing the retrieved set of contentaggregations to the retrieved object network data; identifying anaggregation gap; and generating and sending an alert to a user deviceidentifying the aggregation gap. In some embodiments, identifying anaggregation gap includes identifying topics in the object network datahaving no corresponding content aggregation in the set of contentaggregations.

One aspect of the present disclosure relates to a method for decay basedcontent provisioning. The method includes: identifying a user;retrieving task data from a content library database, which task dataidentifies one or several tasks, and which user task data identifies oneor several skill levels for completion of the one or several tasks;retrieving a user mastery level that identifies a one or several userskill levels; determining a decay profile for the user, wherein thedecay profile comprises a statistical model of the deterioration of theuser skill levels; determining a decay modified mastery level based onthe retrieved user mastery level and the determined decay profile;identifying a plurality of content aggregations for delivery to the userbased on the decay modified mastery level; and providing the pluralityof content aggregations to the user via the user device.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of the user deviceand supervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of a computingstack.

FIG. 9A-9C are schematic illustrations of embodiments of communicationand processing flow of modules within the content distribution network.

FIG. 10A is a flowchart illustrating one embodiment of a process fordata management.

FIG. 10B is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 11 is a flowchart illustrating one embodiment of a process forautomated aggregation delivery.

FIG. 12 is a flowchart illustrating one embodiment of a process fordecay based content provisioning.

FIG. 13 is a flowchart illustrating one embodiment of a process forautomated content aggregation generation.

FIG. 14 is a flowchart illustrating one embodiment of a process forautomated identification of noun phrases in a parse tree.

FIG. 15 is a flowchart illustrating one embodiment of a process forautomated creation of a subset of rational phrases.

FIG. 16 is a flowchart illustrating one embodiment of a detailed processfor automated content aggregation generation.

FIG. 17 is a flowchart illustrating one embodiment of a process forautomated content aggregation evaluation.

FIG. 18 is a flowchart illustrating one embodiment of a process forautomated evaluation of a set of content aggregations.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing unit, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination of computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provide access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming systems, business or homeappliances, and/or personal messaging devices, capable of communicatingover network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such as awireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 112, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including his or her user device 106, contentresources accessed, and interactions with other user devices 106. Thisdata may be stored and processed by the user data server 114, to supportuser tracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO, or the like, or location systems orfeatures including, for example, one or several transceivers that candetermine location of the one or several components of the contentdistribution network 100 via, for example, triangulation. All of theseare depicted as navigation system 122.

In some embodiments, navigation system 122 can include one or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser-based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XMLencryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such aspublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-311 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-311, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-311 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in-is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user mayhave one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, thestudent's learning style can be any learning style describing how thestudent best learns or how the student prefers to learn. In oneembodiment, these learning styles can include, for example,identification of the student as an auditory learner, as a visuallearner, and/or as a tactile learner. In some embodiments, the dataidentifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. In some embodiments, these datapackets in the content library database 303 can be linked to form anobject network. In some embodiments, these data packets can be linked inthe object network according to one or several prerequisiterelationships that can, for example, identify the relative hierarchyand/or difficulty of the data objects. In some embodiments, thishierarchy of data objects can be generated by the content distributionnetwork 100 according to user experience with the object network, and insome embodiments, this hierarchy of data objects can be generated basedon one or several existing and/or external hierarchies such as, forexample, a syllabus, a table of contents, or the like. In someembodiments, for example, the object network can correspond to asyllabus such that content for the syllabus is embodied in the objectnetwork.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network, also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user;relation to a common subject, topic, skill, or the like; creation from acommon set of source material such as source data packets; or the like.In some embodiments, the content aggregation can comprise a grouping ofcontent comprising the presentation portion that can be provided to theuser in the form of, for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets that can be, for example, organized in object network. In someembodiments, the one or several content aggregations can be each createdfrom content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and, in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network can be organized according to one or several tasksidentified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee, or pricing based on network usage. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devices,and the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309,can store information relating to one or several predictive models. Insome embodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Performance Factor Analysis (PFA), or the like. Theevidence model can, in some embodiments, be customizable to a userand/or to one or several content items. Specifically, one or severalinputs relating to the user and/or to one or several content items canbe inserted into the evidence model. These inputs can include, forexample, one or several measures of user skill level, one or severalmeasures of content item difficulty and/or skill level, or the like. Thecustomized evidence model can then be used to predict the likelihood ofthe user providing desired or undesired responses to one or several ofthe content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a user such as a student-user failing to achieve a desiredoutcome such as, for example, failing to correctly respond to one orseveral data packets, failure to achieve a desired level of completionof a program, for example in a pre-defined time period, failure toachieve a desired learning outcome, or the like. In some embodiments,the risk probability can identify the risk of the student-user failingto complete 60% of the program.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of the program such as, for example, a completion stage and/orcompletion status of the program. In one embodiment, for example, thefirst model function can be associated with a first completion status,the second model function can be associated with a second completionstatus, the third model function can be associated with a thirdcompletion status, and the fourth model function can be associated witha fourth completion status. In some embodiments, these completionstatuses can be selected such that some or all of these completionstatuses are less than the desired level of completion of the program.Specifically, in some embodiments, these completion statuses can beselected to all be at less than 60% completion of the program, and morespecifically, in some embodiments, the first completion status can be at20% completion of the program, the second completion status can be at30% completion of the program, the third completion status can be at 40%completion of the program, and the fourth completion status can be at50% completion of the program. Similarly, any desired number of modelfunctions can be associated with any desired number of completionstatuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a student-user's progress through a program.In some embodiments, the student-user's progress can be compared to oneor several status trigger thresholds, each of which status triggerthresholds can be associated with one or more of the model functions. Ifone of the status triggers is triggered by the student-user's progress,the corresponding one or several model functions can be selected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program status parameters. In some embodiments, each of the modelfunctions can use the same program status parameters, and in someembodiments, the model functions can use different program statusparameters. In some embodiments, the model functions use differentprogram status parameters when at least one of the model functions usesat least one program status parameter that is not used by others of themodel functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several students. In someembodiments, this model can identify a single skill level of a studentand/or a range of possible skill levels of a student. In someembodiments, this statistical model can identify a skill level of astudent-user and an error value or error range associated with thatskill level. In some embodiments, the error value can be associated witha confidence interval determined based on a confidence level. Thus, insome embodiments, as the number of student interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

A threshold database 310, also referred to herein as a thresholddatabase, can store one or several threshold values. These one orseveral threshold values can delineate between states or conditions. Inone exemplary embodiment, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education-related data, consumer sales data,health-related data, and the like. Illustrative external dataaggregators 311 may include, for example, social networking web servers,public records data stores, learning management systems, educationalinstitution servers, business servers, consumer sales data stores,medical record data stores, etc. Data retrieved from various externaldata aggregators 311 may be used to verify and update user accountinformation, suggest user content, and perform user and contentevaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering. This embodiment allows reporting timely information thatmight be of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: contentprovided to users; content consumed by users; responses provided byusers; user skill levels; content difficulty levels; next content forproviding to users; etc. The internal components 402-408 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The content management server102 can opt to allow third parties to use internally or externallygathered information that is aggregated within the server 102 bysubscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 238 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time or along another time line. There is a definedAPI between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith response reported that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like SCTP andUDP could be used in some embodiments to intake the gatheredinformation. A protocol such as SSL could be used to protect theinformation over the TCP connection. Authentication and authorizationcan be performed to any user devices 106 and/or supervisor deviceinterfacing to the server 102. The security and/or integration hardware410 receives the information from one or several of the user devices 106and/or the supervisor devices 110 by providing the API and anyencryption, authorization, and/or authentication. In some cases, thesecurity and/or integration hardware 410 reformats or rearranges thisreceived information.

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. Specifically, in some embodiments,the messaging bus 412 can receive and output information from at leastone of the packet selection system, the presentation system, theresponse system, and the summary model system. In some embodiments, thisinformation can be output according to a “push” model, and in someembodiments, this information can be output according to a “pull” model.

As indicated in FIG. 4, processing subscribers are indicated by aconnector to the messaging bus 412, the connector having an arrow headpointing away from the messaging bus 412. Only data streams within themessaging queue 412 that a particular processing subscriber hassubscribed to may be read by that processing subscriber if received atall. Gathered information sent to the messaging queue 412 is processedand returned in a data stream in a fraction of a second by the messagingqueue 412. Various multicasting and routing techniques can be used todistribute a data stream from the messaging queue 412 that a number ofprocessing subscribers have requested. Protocols such as Multicast ormultiple Unicast could be used to distribute streams within themessaging queue 412. Additionally, transport layer protocols like TCP,SCTP and UDP could be used in various embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of message in a particularcategory. For example, a data stream can comprise all of the datareported to the messaging bus 412 by a designated set of components. Oneor more processing subscribers could subscribe and receive the datastream to process the information and make a decision and/or feed theoutput from the processing as gathered information fed back into themessaging queue 412. Through the CC interface 338 a developer can searchthe available data streams or specify a new data stream and its API. Thenew data stream might be determined by processing a number of existingdata streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users, and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regard to the components 402-48, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In some embodiments, these one or several data packets can beadaptively selected and/or selected according to one or severalselection rules. In one embodiment, for example, the recommendationengine can retrieve information from the user profile database 301identifying, for example, a skill level of the user. The recommendationengine can further retrieve information from the content librarydatabase 303 identifying, for example, potential data packets forproviding to the user and the difficulty of those data packets and/orthe skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a student-userbased on one or several received responses from that student-user. Insome embodiments, one or several data packets can be eliminated from thepool of potential data packets if the prediction indicates either toohigh a likelihood of a desired response or too low a likelihood of adesired response. In some embodiments, the recommendation engine canthen apply one or several selection criteria to the remaining potentialdata packets to select a data packet for providing to the user. Theseone or several selection criteria can be based on, for example, criteriarelating to a desired estimated time for receipt of response to the datapacket, one or several content parameters, one or several assignmentparameters, or the like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the summary model system 404 may query one or more databases and/or datastore servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include a response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices 106,supervisor devices 110, and administrator servers 116, etc. Forinstance, response system 406 may receive, aggregate, and analyze userevaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several responses into one or severalobservables can include determining whether the one or several responsesare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several responsesinto one or several observables can include characterizing the degree towhich one or several responses are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features, contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2, and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrative servers 116, and other devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater).

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to the user in perceptible and/orinterpretable form, and may receive one or several inputs from the userby generating one or several electrical signals based on one or severaluser-caused interactions with the I/O subsystem such as the depressingof a key or button, the moving of a mouse, the interaction with atouchscreen or trackpad, the interaction of a sound wave with amicrophone, or the like.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 318 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that, when executed by aprocessor, provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 300 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem532 may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 311). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 via the communication network 120 that caninclude one or several intermediate hubs 604. In some embodiments, thesource hub 602 can be any one or several components of the contentdistribution network generating and initiating the sending of a message,and the terminal hub 606 can be any one or several components of thecontent distribution network 100 receiving and not re-sending themessage. In some embodiments, for example, the source hub 602 can be oneor several of the user device 106, the supervisor device 110, and/or theserver 102, and the terminal hub 606 can likewise be one or several ofthe user device 106, the supervisor device 110, and/or the server 102.In some embodiments, the intermediate hubs 604 can include any computingdevice that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatingly connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a Smartphone, atablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, aSmartphone, a tablet, a Smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7, the user device 106 can include apersonal user device 106-A and one or several other user devices 106-B.In some embodiments, one or both of the personal user device 106-A andthe one or several other user devices 106-B can be communicatinglyconnected to the content management server 102 and/or to the navigationsystem 122. Similarly, the supervisor device 110 can include a personalsupervisor device 110-A and one or several other supervisor devices110-B. In some embodiments, one or both of the personal supervisordevice 110-A and the one or several other supervisor devices 110-B canbe communicatingly connected to the content management server 102 and/orto the navigation system 122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to a page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B and/or account that is actively being used. Ifthe user is not actively using an other device 106-B, 110-B and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an aural, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 8, a schematic illustration of one embodimentof an application stack, and particularly of a stack 650 is shown. Insome embodiments, the content distribution network 100 can comprise aportion of the stack 650 that can include an infrastructure layer 652, aplatform layer 654, an applications layer 656, and a products layer 658.In some embodiments, the stack 650 can comprise some or all of thelayers, hardware, and/or software to provide one or several desiredfunctionalities and/or productions.

As depicted in FIG. 8, the infrastructure layer 652 can include one orseveral servers, communication networks, data stores, privacy servers,and the like. In some embodiments, the infrastructure layer can furtherinclude one or several user devices 106 and/or supervisor devices 110connected as part of the content distribution network.

The platform layer can include one or several platform softwareprograms, modules, and/or capabilities. These can include, for example,identification services, security services, and/or adaptive platformservices 660. In some embodiments, the identification services can, forexample, identify one or several users, components of the contentdistribution network 100, or the like. The security services can monitorthe content distribution network for one or several security threats,breaches, viruses, malware, or the like. The adaptive platform services660 can receive information from one or several components of thecontent distribution network 100 and can provide predictions, models,recommendations, or the like based on that received information. Thefunctionality of the adaptive platform services 660 will be discussed ingreater detail in FIGS. 9A-9C, below.

The applications layer 656 can include software or software modules uponor in which one or several product softwares or product software modulescan operate. In some embodiments, the applications layer 656 caninclude, for example, a management system, record system, or the like.In some embodiments, the management system can include, for example, aLearning Management System (LMS), a Content Management System (CMS), orthe like. The management system can be configured to control thedelivery of one or several resources to a user and/or to receive one orseveral responses from the user. In some embodiments, the records systemcan include, for example, a virtual gradebook, a virtual counselor, orthe like.

The products layer can include one or several software products and/orsoftware module products. These software products and/or software moduleproducts can provide one or several services and/or functionalities toone or several users of the software products and/or software moduleproducts.

With reference now to FIG. 9A-9C, schematic illustrations of embodimentsof communication and processing flow of modules within the contentdistribution network 100 are shown. In some embodiments, thecommunication and processing can be performed in portions of theplatform layer 654 and/or applications layer 656. FIG. 9A depicts afirst embodiment of such communications or processing that can be in theplatform layer 654 and/or applications layer 656 via the message channel412.

The platform layer 654 and/or applications layer 656 can include aplurality of modules that can be embodied in software or hardware. Insome embodiments, some or all of the modules can be embodied in hardwareand/or software at a single location, and in some embodiments, some orall of these modules can be embodied in hardware and/or software atmultiple locations. These modules can perform one or several processesincluding, for example, a presentation process 670, a response process676, a summary model process 680, and a packet selection process 684.

The presentation process 670 can, in some embodiments, include one orseveral methods and/or steps to deliver content to one or several userdevices 106 and/or supervisor devices 110. The presentation process 670can be performed by a presenter module 672 and a view module 674. Thepresenter module 672 can be a hardware or software module of the contentdistribution network 100, and specifically of the server 102. In someembodiments, the presenter module 672 can include one or severalportions, features, and/or functionalities that are located on theserver 102 and/or one or several portions, features, and/orfunctionalities that are located on the user device 106. In someembodiments, the presenter module 672 can be embodied in thepresentation system 408.

The presenter module 672 can control the providing of content to one orseveral user devices 106 and/or supervisor devices 110. Specifically,the presenter module 672 can control the generation of one or severalmessages to provide content to one or several desired user devices 106and/or supervisor devices 110. The presenter module 672 can furthercontrol the providing of these one or several messages to the desiredone or several desired user devices 106 and/or supervisor devices 110.Thus, in some embodiments, the presenter module 672 can control one orseveral features of the communications subsystem 532 to generate andsend one or several electrical signals comprising content to one orseveral user devices 106 and/or supervisor devices 110.

In some embodiments, the presenter module 672 can control and/or managea portion of the presentation functions of the presentation process 670,and can specifically manage an “outer loop” of presentation functions.As used herein, the outer loop refers to tasks relating to the trackingof a user's progress through all or a portion of a group of datapackets. In some embodiments, this can include the identification of oneor several completed data packets or nodes and/or the non-adaptiveselection of one or several next data packets or nodes according to, forexample, one or several fixed rules. Such non-adaptive selection doesnot rely on the use of predictive models, but rather on rulesidentifying next data packets based on data relating to the completionof one or several previously completed data packets or assessmentsand/or whether one or several previously completed data packets weresuccessfully completed.

In some embodiments, and due to the management of the outer loop ofpresentation functions including the non-adaptive selection of one orseveral next data packets, nodes, or tasks by the presenter module, thepresenter module can function as a recommendation engine referred toherein as a first recommendation engine or a rules-based recommendationengine. In some embodiments, the first recommendation engine can beconfigured to select a next node for a user based on one or all of: theuser's current location in the content network; potential next nodes;the user's history including the user's previous responses; and one orseveral guard conditions associated with the potential next nodes. Insome embodiments, a guard condition defines one or several prerequisitesfor entry into, or exit from, a node.

In some embodiments, the presenter module 672 can include a portionlocated on the server 102 and/or a portion located on the user device106. In some embodiments, the portion of the presenter module 672located on the server 102 can receive data packet information andprovide a subset of the received data packet information to the portionof the presenter module 672 located on the user device 106. In someembodiments, this segregation of functions and/or capabilities canprevent solution data from being located on the user device 106 and frombeing potentially accessible by the user of the user device 106.

In some embodiments, the portion of the presenter module 672 located onthe user device 106 can be further configured to receive the subset ofthe data packet information from the portion of the presenter module 672located on the server 102 and provide that subset of the data packetinformation to the view module 674. In some embodiments, the portion ofthe presenter module 672 located on the user device 106 can be furtherconfigured to receive a content request from the view module 674 and toprovide that content request to the portion of the presenter module 674located on the server 102.

The view module 674 can be a hardware or software module of some or allof the user devices 106 and/or supervisor devices 110 of the contentdistribution network 100. The view module 674 can receive one or severalelectrical signals and/or communications from the presenter module 672and can provide the content received in those one or several electricalsignals and/or communications to the user of the user device 106 and/orsupervisor device 110 via, for example, the I/O subsystem 526.

In some embodiments, the view module 674 can control and/or monitor an“inner loop” of presentation functions. As used herein, the inner looprefers to tasks relating to the tracking and/or management of a user'sprogress through a data packet. This can specifically relate to thetracking and/or management of a user's progression through one orseveral pieces of content, questions, assessments, and/or the like of adata packet. In some embodiments, this can further include the selectionof one or several next pieces of content, next questions, nextassessments, and/or the like of the data packet for presentation and/orproviding to the user of the user device 106.

In some embodiments, one or both of the presenter module 672 and theview module 674 can comprise one or several presentation engines. Insome embodiments, these one or several presentation engines can comprisedifferent capabilities and/or functions. In some embodiments, one of thepresentation engines can be configured to track the progress of a userthrough a single data packet, task, content item, or the like, and insome embodiments, one of the presentation engines can track the progressof a user through a series of data packets, tasks, content items, or thelike.

The response process 676 can comprise one or several methods and/orsteps to evaluate a response. In some embodiments, this can include, forexample, determining whether the response comprises a desired responseand/or an undesired response. In some embodiments, the response process676 can include one or several methods and/or steps to determine thecorrectness and/or incorrectness of one or several received responses.In some embodiments, this can include, for example, determining thecorrectness and/or incorrectness of a multiple choice response, atrue/false response, a short answer response, an essay response, or thelike. In some embodiments, the response processor can employ, forexample, natural language processing, semantic analysis, or the like indetermining the correctness or incorrectness of the received responses.

In some embodiments, the response process 676 can be performed by aresponse processor 678. The response processor 678 can be a hardware orsoftware module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the responseprocessor 678 can be embodied in the response system 406. In someembodiments, the response processor 678 can be communicatingly connectedto one or more of the modules of the presentation process 760 such as,for example, the presenter module 672 and/or the view module 674. Insome embodiments, the response processor 678 can be communicatinglyconnected with, for example, the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The summary model process 680 can comprise one or several methods and/orsteps to generate and/or update one or several models. In someembodiments, this can include, for example, implementing informationreceived either directly or indirectly from the response processor 678to update one or several models. In some embodiments, the summary modelprocess 680 can include the update of a model relating to one or severaluser attributes such as, for example, a user skill model, a userknowledge model, a learning style model, or the like. In someembodiments, the summary model process 680 can include the update of amodel relating to one or several content attributes including attributesrelating to a single content item and/or data packet and/or attributesrelating to a plurality of content items and/or data packets. In someembodiments, these models can relate to an attribute of the one orseveral data packets such as, for example, difficulty, discrimination,required time, or the like.

In some embodiments, the summary model process 680 can be performed bythe model engine 682. In some embodiments, the model engine 682 can be ahardware or software module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the model engine682 can be embodied in the summary model system 404.

In some embodiments, the model engine 682 can be communicatinglyconnected to one or more of the modules of the presentation process 760such as, for example, the presenter module 672 and/or the view module674, can be connected to the response processor 678 and/or therecommendation. In some embodiment, the model engine 682 can becommunicatingly connected to the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The packet selection process 684 can comprise one or several stepsand/or methods to identify and/or select a data packet for presentationto a user. In some embodiments, this data packet can comprise aplurality of data packets. In some embodiments, this data packet can beselected according to one or several models updated as part of thesummary model process 680. In some embodiments, this data packet can beselected according to one or several rules, probabilities, models, orthe like. In some embodiments, the one or several data packets can beselected by the combination of a plurality of models updated in thesummary model process 680 by the model engine 682. In some embodiments,these one or several data packets can be selected by a recommendationengine 686. The recommendation engine 686 can be a hardware or softwaremodule of the content distribution network 100, and specifically of theserver 102. In some embodiments, the recommendation engine 686 can beembodied in the packet selection system 402. In some embodiments, therecommendation engine 686 can be communicatingly connected to one ormore of the modules of the presentation process 670, the responseprocess 676, and/or the summary model process 680 either directly and/orindirectly via, for example, the message channel.

In some embodiments, and as depicted in FIG. 9A, a presenter module 672can receive a data packet for presentation to a user device 106. Thisdata packet can be received, either directly or indirectly from arecommendation engine 686. In some embodiments, for example, thepresenter module 672 can receive a data packet for providing to a userdevice 106 from the recommendation engine 686, and in some embodiments,the presenter module 672 can receive an identifier of a data packet forproviding to a user device 106 via a view module 674. This can bereceived from the recommendation engine 686 via a message channel 412.Specifically, in some embodiments, the recommendation engine 686 canprovide data to the message channel 412 indicating the identificationand/or selection of a data packet for providing to a user via a userdevice 106. In some embodiments, this data indicating the identificationand/or selection of the data packet can identify the data packet and/orcan identify the intended recipient of the data packet.

The message channel 412 can output this received data in the form of adata stream 690 which can be received by, for example, the presentermodule 672, the model engine 682, and/or the recommendation engine 686.In some embodiments, some or all of: the presenter module 672, the modelengine 682, and/or the recommendation engine 686 can be configured toparse and/or filter the data stream 690 to identify data and/or eventsrelevant to their operation. Thus, for example, the presenter module 672can be configured to parse the data stream for information and/or eventsrelevant to the operation of the presenter module 672.

In some embodiments, the presenter module 672 can extract the datapacket from the data stream 690 and/or extract data identifying the datapacket and/or indicating the selecting of a data packet from the datastream. In the event that data identifying the data packet is extractedfrom the data stream 690, the presenter module 672 can request andreceive the data packet from the database server 104, and specificallyfrom the content library database 303. In embodiments in which dataindicating the selection of a data packet is extracted from the datastream 690, the presenter module 672 can request and receiveidentification of the data packet from the recommendation engine 686 andthen request and receive the data packet from the database server 104,and specifically from the content library database 303, and in someembodiments in which data indicating the selection of a data packet isextracted from the data stream 690, the presenter module 672 can requestand receive the data packet from the recommendation engine 686.

The presenter module can then provide the data packet and/or portions ofthe data packet to the view module 674. In some embodiments, forexample, the presenter module 672 can retrieve one or several rulesand/or conditions that can be, for example, associated with the datapacket and/or stored in the database server 104. In some embodiments,these rules and/or conditions can identify portions of a data packet forproviding to the view module 674 and/or portions of a data packet to notprovide to the view module 674. In some embodiments, for example,sensitive portions of a data packet, such as, for example, solutioninformation to any questions associated with a data packet, is notprovided to the view module 674 to prevent the possibility of undesiredaccess to those sensitive portions of the data packet. Thus, in someembodiments, the one or several rules and/or conditions can identifyportions of the data packet for providing to the view module 674 and/orportions of the data packet for not providing to the view module.

In some embodiments, the presenter module 672 can, according to the oneor more rules and/or conditions, generate and transmit an electronicmessage containing all or portions of the data packet to the view module674. The view module 674 can receive these all or portions of the datapacket and can provide all or portions of this information to the userof the user device 106 associated with the view module 674 via, forexample, the I/O subsystem 526. In some embodiments, as part of theproviding of all or portions of the data packet to the user of the viewmodule 674, one or several user responses can be received by the viewmodule 674. In some embodiments, these one or several user responses canbe received via the I/O subsystem 526 of the user device 106.

After one or several user responses have been received, the view module674 can provide the one or several user responses to the responseprocessor 678. In some embodiments, these one or several responses canbe directly provided to the response processor 678, and in someembodiments, these one or several responses can be provided indirectlyto the response processor 678 via the message channel 412.

After the response processor 678 receives the one or several responses,the response processor 678 can determine whether the responses aredesired responses and/or the degree to which the received responses aredesired responses. In some embodiments, the response processor can makethis determination via, for example, use of one or several techniques,including, for example, natural language processing (NLP), semanticanalysis, or the like.

In some embodiments, the response processor can determine whether aresponse is a desired response and/or the degree to which a response isa desired response with comparative data which can be associated withthe data packet. In some embodiments, this comparative data cancomprise, for example, an indication of a desired response and/or anindication of one or several undesired responses, a response key, aresponse rubric comprising one criterion or several criteria fordetermining the degree to which a response is a desired response, or thelike. In some embodiments, the comparative data can be received as aportion of and/or associated with a data packet. In some embodiments,the comparative data can be received by the response processor 678 fromthe presenter module 672 and/or from the message channel 412. In someembodiments, the response data received from the view module 674 cancomprise data identifying the user and/or the data packet or portion ofthe data packet with which the response is associated. In someembodiments in which the response processor 678 merely receives dataidentifying the data packet and/or portion of the data packet associatedwith the one or several responses, the response processor 678 canrequest and/or receive comparative data from the database server 104,and specifically from the content library database 303 of the databaseserver 104.

After the comparative data has been received, the response processor 678determines whether the one or several responses comprise desiredresponses and/or the degree to which the one or several responsescomprise desired responses. The response processor can then provide thedata characterizing whether the one or several responses comprisedesired responses and/or the degree to which the one or severalresponses comprise desired responses to the message channel 412. Themessage channel can, as discussed above, include the output of theresponse processor 678 in the data stream 690 which can be constantlyoutput by the message channel 412.

In some embodiments, the model engine 682 can subscribe to the datastream 690 of the message channel 412 and can thus receive the datastream 690 of the message channel 412 as indicated in FIG. 9A. The modelengine 682 can monitor the data stream 690 to identify data and/orevents relevant to the operation of the model engine. In someembodiments, the model engine 682 can monitor the data stream 690 toidentify data and/or events relevant to the determination of whether aresponse is a desired response and/or the degree to which a response isa desired response.

When a relevant event and/or relevant data are identified by the modelengine, the model engine 682 can take the identified relevant eventand/or relevant data and modify one or several models. In someembodiments, this can include updating and/or modifying one or severalmodels relevant to the user who provided the responses, updating and/ormodifying one or several models relevant to the data packet associatedwith the responses, and/or the like. In some embodiments, these modelscan be retrieved from the database server 104, and, in some embodiments,can be retrieved from the model data source 309 of the database server104.

After the models have been updated, the updated models can be stored inthe database server 104. In some embodiments, the model engine 682 cansend data indicative of the event of the completion of the model updateto the message channel 412. The message channel 412 can incorporate thisinformation into the data stream 690 which can be received by therecommendation engine 686. The recommendation engine 686 can monitor thedata stream 690 to identify data and/or events relevant to the operationof the recommendation engine 686. In some embodiments, therecommendation engine 686 can monitor the data stream 690 to identifydata and/or events relevant to the updating of one or several models bythe model engine 682.

When the recommendation engine 686 identifies information in the datastream 690 indicating the completion of the summary model process 680for models relevant to the user providing the response and/or for modelsrelevant to the data packet provided to the user, the recommendationengine 686 can identify and/or select a next data packet for providingto the user and/or to the presentation process 470. In some embodiments,this selection of the next data packet can be performed according to oneor several rules and/or conditions. After the next data packet has beenselected, the recommendation engine 686 can provide information to themodel engine 682 identifying the next selected data packet and/or to themessage channel 412 indicating the event of the selection of the nextcontent item. After the message channel 412 receives informationidentifying the selection of the next content item and/or receives thenext content item, the message channel 412 can include this informationin the data stream 690 and the process discussed with respect to FIG. 9Acan be repeated.

With reference now to FIG. 9B, a schematic illustration of a secondembodiment of communication or processing that can be in the platformlayer 654 and/or applications layer 656 via the message channel 412 isshown. In the embodiment depicted in FIG. 9B, the data packet providedto the presenter module 672 and then to the view module 674 does notinclude a prompt for a user response and/or does not result in thereceipt of a user response. As no response is received, when the datapacket is completed, nothing is provided to the response processor 678,but rather data indicating the completion of the data packet is providedfrom one of the view module 674 and/or the presenter module 672 to themessage channel 412. The data is then included in the data stream 690and is received by the model engine 682 which uses the data to updateone or several models. After the model engine 682 has updated the one orseveral models, the model engine 682 provides data indicating thecompletion of the model updates to the message channel 412. The messagechannel 412 then includes the data indicating the completion of themodel updates in the data stream 690 and the recommendation engine 686,which can subscribe to the data stream 690, can extract the dataindicating the completion of the model updates from the data stream 690.The recommendation engine 686 can then identify a next one or severaldata packets for providing to the presenter module 672, and therecommendation engine 686 can then, either directly or indirectly,provide the next one or several data packets to the presenter module672.

In some embodiments, of the communication as shown in FIGS. 9A and 9B,all communications between any of the presenter module 672, the responseprocessor 678, the model engine 682, and the recommendation engine 686can pass through the message channel 412. Alternatively, in someembodiments, some of the communications between any of the presentermodule 672, the response processor 678, the model engine 682, and therecommendation engine 686 can pass through the message channel andothers of the communications between any of the presenter module 672,the response processor 678, the model engine 682, and the recommendationengine 686 can be direct.

With reference now to FIG. 9C, a schematic illustration of an embodimentof dual communication, or hybrid communication, in the platform layer654 and/or applications layer 656 is shown. Specifically, in thisembodiment, some communication is synchronous with the completion of oneor several tasks and some communication is asynchronous. Thus, in theembodiment depicted in FIG. 9C, the presenter module 972 communicatessynchronously with the model engine 682 via a direct communication 692and communicates asynchronously with the model engine 682 via themessage channel 412.

In some embodiments, and as depicted in FIG. 9C, the synchronouscommunication and/or the operation of the presenter module 672, theresponse processor 678, the model engine 682, and the recommendationengine 686 can be directed and/or controlled by a controller. In someembodiments, this controller can be part of the server 102 and/orlocated in any one or more of the presenter module 672, the responseprocessor 678, the model engine 682, and the recommendation engine 686.In some embodiments, this controller can be located in the presentermodule 672, which presenter module can control communications with andbetween itself and the response processor 678, the model engine 682, andthe recommendation engine 686, and the presenter module can thus controlthe functioning of the response processor 678, the model engine 682, andthe recommendation engine 686.

Specifically, and with reference to FIG. 9C, the presenter module 672can receive and/or select a data packet for presentation to the userdevice 106 via the view module 674. In some embodiments, the presentermodule 672 can identify all or portions of the data packet that can beprovided to the view module 674 and portions of the data packet forretaining from the view module 674. In some embodiments, the presentermodule can provide all or portions of the data packet to the view module674. In some embodiments, and in response to the receipt of all orportions of the data packet, the view module 674 can provide aconfirmation of receipt of the all or portions of the data packet andcan provide those all or portions of the data packet to the user via theuser device 106. In some embodiments, the view module 674 can providethose all or portions of the data packet to the user device 106 whilecontrolling the inner loop of the presentation of the data packet to theuser via the user device 106.

After those all or portions of the data packet have been provided to theuser device 106, a response indicative of the completion of one orseveral tasks associated with the data packet can be received by theview module 674 from the user device 106, and specifically from the I/Osubsystem 526 of the user device 106. In response to this receive, theview module 674 can provide an indication of this completion status tothe presenter module 672 and/or can provide the response to the responseprocessor 678.

After the response has been received by the response processor 678, theresponse processor 678 can determine whether the received response is adesired response. In some embodiments, this can include, for example,determining whether the response comprises a correct answer and/or thedegree to which the response comprises a correct answer.

After the response processor has determined whether the receivedresponse is a desired response, the response processor 678 can providean indicator of the result of the determination of whether the receivedresponse is a desired response to the presenter module 672. In responseto the receipt of the indicator of whether the result of thedetermination of whether the received response is a desired response,the presenter module 672 can synchronously communicate with the modelengine 682 via a direct communication 692 and can asynchronouslycommunicate with model engine 682 via the message channel 412. In someembodiments, the synchronous communication can advantageously includetwo-way communication between the model engine 682 and the presentermodule 672 such that the model engine 682 can provide an indication tothe presenter module 672 when model updating is completed by the modelengine.

After the model engine 682 has received one or both of the synchronousand asynchronous communications, the model engine 682 can update one orseveral models relating to, for example, the user, the data packet, orthe like. After the model engine 682 has completed the updating of theone or several models, the model engine 682 can send a communication tothe presenter module 672 indicating the completion of the updated one orseveral modules.

After the presenter module 672 receives the communication indicating thecompletion of the updating of the one or several models, the presentermodule 672 can send a communication to the recommendation engine 686requesting identification of a next data packet. As discussed above, therecommendation engine 686 can then retrieve the updated model andretrieve the user information. With the updated models and the userinformation, the recommendation engine can identify a next data packetfor providing to the user, and can provide the data packet to thepresenter module 672. In some embodiments, the recommendation engine 686can further provide an indication of the next data packet to the modelengine 682, which can use this information relating to the next datapacket to update one or several models, either immediately, or afterreceiving a communication from the presenter module 672 subsequent tothe determination of whether a received response for that data packet isa desired response.

With reference now to FIG. 9D, a schematic illustration of oneembodiment of the presentation process 670 is shown. Specifically, FIG.9D depicts multiple portions of the presenter module 672, namely, theexternal portion 673 and the internal portion 675. In some embodiments,the external portion 673 of the presenter module 672 can be located inthe server, and in some embodiments, the internal portion 675 of thepresenter module 672 can be located in the user device 106. In someembodiments, the external portion 673 of the presenter module can beconfigured to communicate and/or exchange data with the internal portion675 of the presenter module 672 as discussed herein. In someembodiments, for example, the external portion 673 of the presentermodule 672 can receive a data packet and can parse the data packet intoportions for providing to the internal portion 675 of the presentermodule 672 and portions for not providing to the internal portion 675 ofthe presenter module 672. In some embodiments, the external portion 673of the presenter module 672 can receive a request for additional dataand/or an additional data packet from the internal portion 675 of thepresenter module 672. In such an embodiment, the external portion 673 ofthe presenter module 672 can identify and retrieve the requested dataand/or the additional data packet from, for example, the database server104 and more specifically from the content library database 104.

With reference now to FIG. 10A, a flowchart illustrating one embodimentof a process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the presentation system 408 and/or by thepresentation module or presentation engine. In some embodiments, theprocess 440 can be performed as part of the presentation process 670.

The process 440 begins at block 442, wherein a data packet isidentified. In some embodiments, the data packet can be a data packetfor providing to a student-user. In some embodiments, the data packetcan be identified based on a communication received either directly orindirectly from the recommendation engine 686.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the presentation system408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to thestudent user, which content can include one or several requests and/orquestions and/or the like. In some embodiments, one of these datacomponents, referred to herein as a response component, can include dataused in evaluating one or several responses received from the userdevice 106 in response to the data packet, and specifically in responseto the presentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as thestudent-user via the user device 106. In some embodiments, the deliverypacket can include the presentation component, and in some embodiments,the delivery packet can exclude the response packet. After the deliverydata packet has been generated, the process 440 proceeds to block 452,wherein the delivery data packet is provided to the user device 106 andmore specifically to the view module 674. In some embodiments, this caninclude providing the delivery data packet to the user device 106 via,for example, the communication network 120.

After the delivery data packet has been provided to the user device 106,the process 440 proceeds to block 454, wherein the data packet and/orone or several components thereof are sent to and/or provided to theresponse processor 678. In some embodiments, this sending of the datapacket and/or one or several components thereof to the responseprocessor can include receiving a response from the student-user, andsending the response to the student-user to the response processorsimultaneous with the sending of the data packet and/or one or severalcomponents thereof to the response processor. In some embodiments, forexample, this can include providing the response component to theresponse processor. In some embodiments, the response component can beprovided to the response processor from the presentation system 408.

With reference now to FIG. 10B, a flowchart illustrating one embodimentof a process 460 for evaluating a response is shown. In someembodiments, the process can be performed as a part of the responseprocess 676 and can be performed by, for example, the response system406 and/or by the response processor 678. In some embodiments, theprocess 460 can be performed by the response system 406 in response tothe receipt of a response, either directly or indirectly, from the userdevice 106 or from the view module 674.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

After the response type has been identified, the process 460 proceeds toblock 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the receive response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more aplurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

With reference now to FIG. 11, a flowchart illustrating one embodimentof a process 700 for automated aggregation delivery is shown. Theprocess 700 can be performed by some or all of the components of thecontent distribution network 100 including by, for example, one orseveral servers 102. In some embodiments, these one or several servers102 can comprise one or several remote resources such as can occur viacloud computing or distributed processing. The process 700 begins atblock 702 wherein a user identifier is received and/or retrieved. Insome embodiments, the user identifier can be received from the userdevice 106 and/or from the supervisor device 110. In some embodiments,for example, the user identifier can be provided to the user device 106and/or supervisor device 110 via the I/O subsystem 526 and can becommunicated to the server 102 via the communications subsystem 532 andthe communication network 120. In some embodiments, the user identifiercan comprise a user ID, password, a username, or the like.

After the user identifier has been received, the process 700 proceeds toblock 704 wherein user data is retrieved. In some embodiments, this caninclude, for example, identifying the user associated with the useridentifier, and retrieving user data associated with that user from oneof the data stores 104, and specifically, in some embodiments, from theuser profile data store 301.

After the user data has been retrieved, the process 700 proceeds toblock 706 wherein user task data is retrieved and/or received. In someembodiments, the user task data can specify one or several tasks for theuser to begin and/or complete. In some embodiments, the user tasks datacan identify one or several skills, skill levels, or the like for thesuccessful completion of the one or several tasks. In some embodiments,these one or several skills, skill levels, or the like can correspondto: skills required to successfully complete the task; a difficulty ordifficulty level of the task; or the like. In some embodiments, such oneor several tasks can correspond to a single data packet and in someembodiments, such one or several tasks can correspond to a plurality ofdata packets. In some embodiments, the task can relate to the content ofone or several data packets but can be an experience or event that isnot embodied in a data packet. In some embodiments, the task can bedetermined by the server 102 by retrieving information from the contentlibrary data store 303 identifying one or several tasks such as, forexample, a schedule and/or syllabus. In some embodiments, the server 102can determine the user's progress through the schedule and/or syllabusand can identify a current incompletion task and/or the next task. Insome embodiments, the server 102 can then retrieve data relevant to thatdetermined task which data can identify, for example, one or severalcontent objects relevant and/or related to that task. In someembodiments, these one or several content objects relevant and/orrelated to the task or data associated with those one or several contentobjects can comprise the user task data.

In some embodiments, the user task data can be retrieved from thecontent library database 303 based on one or several user inputsidentifying one or several tasks or objectives. In some embodiments, forexample, the user can provide an input to the user device 106identifying one or several tasks for which content aggregation deliveryis desired. In response to this received input, the server 102 can querythe content library database 303 for information relating to theidentified one or several tasks. The server 102 can then retrieve datarelevant to that determined task which data can identify, for example,one or several content objects relevant and/or related to that task. Insome embodiments, these one or several content objects relevant and/orrelated to the task or data associated with those one or several contentobjects can comprise the user task data.

In some embodiments, the retrieval of user task data can further includeidentifying one or several skills and/or skill levels relevant to theidentified task. In some embodiments, for example, these relevant skillsand/or skill levels can be determined based on the one or severalcontent items relevant to and/or related to the task. In someembodiments, these relevant skills and/or skill levels can be determinedbased on information associated with the task such as, for example, taskmetadata.

After the user task data has been retrieved, the process 700 proceeds toblock 708 wherein a user time constraint parameter is received and/orretrieved. In some embodiments, user time constraint parameter canidentify, for example, the amount of time before the required start orcompletion of the relevant task identified at block 708. In someembodiments, the user time constraint parameter can be absolute and cantrack the physical amount of time between the time of performing block708 as determined by one or several clocks or timestamps associated withthe server 102 and/or data received by the server 102 and the time forthe start or completion of the task identified at block 706. In someembodiments, the user time constraint parameter can comprise dataidentifying a user defined amount of available time such as, forexample, an amount of time the user can immediately dedicate to the taskand/or to preparation for the task. In such an embodiment, the user candefine the amount of available time via an input at the user device 106that can be provided to the server 102. In some embodiments, the usertime constraint parameter can be based on historical time usageinformation by the user of the content distribution network 100. In someembodiments, for example, the server 102 can retrieve information fromthe user profile data store 301 identifying the amount of time the userhas interacted with the content distribution network 100 and during aperiod of time such as, for example, during the past one or severaldays, weeks, months, and/or years. The content distribution network 102can further, based on other information relevant to the user andcontained in the user profile data store 301 such as, for example,information identifying other tasks for completion by the user,determine an estimated amount of user available time for the taskidentified at block 706.

After the user time constraint parameter has been received, the process700 proceeds to block 710 wherein user mastery data is received and/orretrieved. In some embodiments, this mastery data can identify one orseveral skills and/or skill levels of the user. In some embodiments, theidentified one or several skills and/or skill levels can correspond tothe one or several skills and/or skill levels associated with the taskand that were determined at block 706. In some embodiment, the masterdata can be retrieved from the database server 104, and specifically canbe retrieved from the user profile data store 301.

After the master data has been received and/or retrieved, the process700 proceeds to block 712 wherein presentation data is selected. In someembodiments, the presentation data can comprise one or several contentaggregations for presentation to the user. In some embodiments, thesecontent aggregations can be generated by the user and/or can begenerated from a source other than the user. In some embodiments, theseone or several content aggregations can be associated with the user andcan be stored in the database server 104 and specifically in the userprofile database store 301 and/or the content library data store 303. Insome embodiments, the presentation data can be selected to maximize userpreparation for the task identified at block 706, and specifically toincrease the user's one or several skills and/or skill levels that mostgreatly differ from the one or several skills and/or skill levelsassociated with the task.

After the presentation data has been selected, the process 700 proceedsto block 714, wherein the order of the presentation data is determined.In some embodiments, the order of the presentation data can comprise anorder in which the content aggregations are presented to the user and/ora frequency with which the content aggregations are presented to theuser. In some embodiments, the frequency with which one or several ofthe content aggregations are repeatedly presented to the user caninclude a desired time interval between repeated presentations of one orseveral content aggregations and/or a number of desired intermediatelypresented content aggregations before one or several of the contentaggregations are repeatedly presented.

In some embodiments, the order of presentation of the contentaggregations and/or the frequency with which one or several of thecontent aggregations are presented can be based on one or severalattributes of the user such as, for example, one or several skills orskill levels, or the like. In some embodiments, the order ofpresentation can vary during the time that the content aggregations arebeing presented to the user, and particularly the order of presentationand/or the frequency of presentation can vary based on responsesreceived from the user in response during the presentation of contentaggregations to the user. In some embodiments, for example, the userskill and/or skill level can change based on one or several responsesprovided by the user, and thus the order of presentation and/or thefrequency of presentation can change in response to this changed skilland/or skill level.

In some embodiments, the order and/or frequency of the presentation ofthe content aggregations can be controlled according to an orderingalgorithm. In some embodiments, this can include, for example, a LeitnerBox algorithm. In some embodiments, for example, each of the contentaggregations of the presentation data can be arranged into one orseveral sets or groups based on, for example, a user skill or skilllevel relevant to that content aggregation. In some embodiments, forexample, each of the content aggregations of the presentation data canbe arranged into one or several sets or groups based on user responsedata for that content aggregation, and specifically whether the usercorrectly or incorrectly responded to that content aggregation and/orthe frequency of user correct or incorrect responses to that contentaggregation.

In some embodiments, and before a content aggregation is provided to auser, the content aggregation can be placed in one of the one or severalsets based on the user skill level relevant to that content aggregation.In some embodiments, and as that content aggregation is provided to theuser and responses to that content aggregation are received, the contentaggregation can be moved to a different one of the one or several setsof content aggregations. In some embodiments, for example, if the userincorrectly responds to the content aggregation, then the contentaggregation can be demoted to a “lower” set of content aggregationsbased either on the incorrect response or the decrease in the user skilllevel resulting from the incorrect response. Similarly, in someembodiments, for example, if the user correctly responds to the contentaggregation, then the content aggregation can be promoted to a “higher”set of content aggregations based either on the correct response or theincrease in the user skill level resulting from the correct response. Insome embodiments, each of these sets can be associated with a frequencyof presentation to the user such that “lower” sets are more frequentlypresented to the user and “higher” sets are less frequently presented tothe user.

In some embodiments, the order and frequency algorithm can include oneor several rules that can, in some circumstances limit the frequencythat one or several content aggregations can be presented. In someembodiments, for example, the order and frequency algorithm can includeone or several threshold values specifying a maximum allowable frequencyand/or a minimum allowable amount of time between repeat presentationsof a content aggregation. In some embodiments, when a contentaggregation is selected for repeat presentation, the order and frequencyalgorithm can retrieve one or several thresholds and data identifyingthe frequency of presentation of the selected content aggregation and/orthe time of the last presentation of the content aggregation. The orderand frequency algorithm can compare the retrieved one or severalthresholds and data identifying the frequency of presentation of theselected content aggregation and/or the time of the last presentation ofthe content aggregation to determine if the repeat presentation of theselected content aggregation is allowed. If the repeat presentation ofthe content aggregation is allowed, then that content aggregation can beselected for presentation.

If the repeat presentation of the content aggregation is not allowed,the order and frequency algorithm can retrieve one or several rules toselect an alternative content aggregation for presentation. In someembodiments, these rules can specify the selecting of a new contentaggregation for presentation from the same set of content aggregationsfrom which the previously selected content aggregation was selected. Ifnone of the content aggregations in the same set of content aggregationsfrom which the previously selected content aggregation was selected areallowed for presentation, then the rules can specify the selection of acontent aggregation from a “lower” set of content aggregations. Thesesteps can be repeated until an allowable content aggregation isidentified and/or until it is determined that no content aggregation isallowable for presentation. In the event that no content aggregation isallowable for presentation, then the content aggregation closest tobeing allowable for presentation can be selected and/or a contentaggregation can be selected at random.

After the order and/or frequency of the presentation data has beendetermined, the process 700 proceeds to block 716, wherein thepresentation date is provided to the user. In some embodiments, thepresentation data can be provided to the user by the generation andsending of one or several electrical signals comprising the presentationdata. In some embodiments, these one or several electrical signals canbe generated by the server 102 and can be sent to the user device via,for example, the communication network 120 and the communicationsubsystem 532. In some embodiments, these electrical signals can be sentin the form of the communication such as an alert as discussed above. Insome embodiments, the steps of blocks 714 and 716 can be performedbefore any content aggregation is presented to the user, and can also beperformed before each content aggregation is presented to the user.

With reference now to FIG. 12, a flowchart illustrating one embodimentof a process 800 for decay based content provisioning is shown. Theprocess 800 can be performed by all of the components of the contentdistribution network 100 including by, for example, one or severalservers 102. In some embodiments, these one or several servers 102 cancomprise one or several remote resources such as can occur via cloudcomputing or distributed processing. The process 800 begins at block 802wherein a task is retrieved. In some embodiments, this can correspond tothe step of block 706 of process 700. After the test has been retrieved,the process 800 proceeds to block 804 wherein one or several taskobjectives are identified. In some embodiments, these task objectivescan identify one or several skills and/or skill levels desired and/orrequired for completion of the task. In some embodiments, these one orseveral skills or skill levels can be identified in the user task dataand/or associated with the task retrieved in block 802.

After the task objectives have been identified, the process 800 proceedsto block 806 wherein user history is retrieved. In some embodiments,user history can be retrieved from the database server 104, andspecifically from the user profile database 301. In some embodiments,user history can comprise one or several models such as, for example, alearning model and/or decay model. In some embodiments, these one orseveral models can date one or several user attributes such as, forexample, one or several user skills and/or user skill levels.

After the user history has been retrieved, the process 800 proceeds toblock 808 wherein a user mastery level is retrieved and/or determined.In some embodiments, the user history data can comprise informationidentifying a mastery level indicative of one or several user skillsand/or user skill levels determined based on user interaction with thecontent distribution network 100 such as, for example, user interactionwith one or several data packets, and/or information relating to theuser input into the content distribution network 100. In someembodiments, determining the user mastery level can comprise extractinginformation identifying the mastery level from the user history data.

After the user mastery level has been determined and/or retrieved, theprocess 800 proceeds to block 810 wherein the user decay profile isdetermined. In some embodiments, the user decay profile is based on adecay model and/or is a decay model. In some embodiments, the decaymodel can comprise a statistical model identifying and/or predicting arate at which one or several of the user's skills or skill levelsdeteriorate and/or decrease. In some embodiments, this decay model canbe based on one or several user attributes that can be, for example,identified in the user history. These user attributes can include, forexample, one or several preferences, learning styles, cognitive abilityor intelligence levels, past skill or skill level decay, or the like.

After the decay profile has been determined, the process 800 proceeds toblock 812 wherein a decay modified mastery level is determined. In someembodiments, this can include adjusting the retrieved mastery level ofblock 808 with the decay profile according to one or several userrelevant parameters such as, for example, the amount of time between thedetermination of the mastery level retrieved in block 808 and thedetermination of the decay modified mastery level in block 812, userinteractions or lack of interactions with content distribution networkbetween the determination of the mastery level retrieved in block 808and the determination of the decay modified mastery level in block 812,or the like. In some embodiments, the determination of the decaymodified mastery level can include retrieving and/or determining theseone or several parameters based on the user history retrieved in block806, inputting these one or several parameters into the decay profileand/or decay model, inputting the retrieved mastery level into the decayprofile, and outputting from the decay profile a decay modified masterylevel. In some embodiments, the determination of the decay modifiedmastery level can be performed by the processor 102, and can beperformed by the model engine 682 and/or the summary model system 404.

After the decay modified mastery level has been determined, the process800 proceeds to block 814 wherein the processing time to reach and/orattain the task objectives identified in block 804 is determined. Insome embodiments, this can include a determination of the differencebetween the decay modified mastery level and the one or several skillsand/or skill levels embodied therein and the one or several skills orskill levels embodied in the task objectives. In some embodiments, thisdifference between the decay modified mastery level and the one orseveral skills and/or skill levels embodied in the task objectives canbe input into the user model, or more specifically into the learningmodel. In some embodiments, the user model can output an estimatedprocessing time for the achievement of the task objectives.

After the processing time to reach the one or several task objectiveshas been determined, the process 800 proceeds to block 816 wherein oneor several potential content aggregations are identified. In someembodiments, the content aggregations can be generated by the userand/or can be generated by a source other than the user. In someembodiments, these one or several content aggregations can be stored inthe database server 104, and particularly within the content librarydatabase 303 and/or the user profile database 301 of the database server104. In some embodiments, these one or several content aggregations cancorrespond and/or relate to the one or several task objectives and/orthe one or several skills and/or skill levels associated with those oneor several task objectives. In some embodiments, potential contentaggregations can be identified based on information associated withthose one or several content aggregations and linking those one orseveral content aggregations with the task objectives. In someembodiments, potential content aggregations can be further identifiedbased on, for example, data indicating the efficacy of the contentaggregations, data indicating time requirements associated with thecontent aggregations, user feedback relating to the contentaggregations, or the like.

After the potential content aggregations have been identified, theprocess 800 proceeds to block 818 wherein a path is mapped based on thedecay modified mastery level and the task objectives. In someembodiments, the path can comprise the order presentation of identifiedpotential content aggregations. In some embodiments, for example, thepotential content aggregations can be associated with content and/ordata packets which can be ordered in a hierarchical relationship. Insome embodiments, the path can be mapped based on these hierarchicalrelationships so as to provide an ordering for delivery of the potentialcontent aggregations corresponding to the hierarchical relationships ofthose content aggregations and/or of the associated related content. Insome embodiments, the path can be mapped by the server 102, andspecifically by recommendation engine 686 and/or the packet selectionsystem 402.

After the path has been mapped, the process 800 proceeds to block 820wherein one or several content aggregations are selected for delivery.In some embodiments, content aggregations selected for delivery cancorrespond to the first content aggregation in the map that wasdetermined at block 818. In some embodiments, the content aggregationsselected for delivery can be selected by the server 102, andspecifically by the recommendation engine 686 and/or the packetselection system 402.

After the content aggregations have been selected for delivery, theprocess 800 proceeds to block 822 wherein the content aggregations aredelivered. In some embodiments, the delivery of the content aggregationcan comprise a delivery of one or several electrical signals containingdata from the content aggregation to the user device 106 and thepresentation of the content aggregation to the user of the user devicevia the I/O subsystem 526. In some embodiments, for example, this caninclude the delivery, either directly or indirectly, of the contentaggregation from the recommendation engine 686 to the present module 672and then to the view module 674. In some embodiments, and after thedelivery of the content aggregation, the user can provide one or severalresponses to the content aggregation, which responses can be passed,either directly or indirectly to the response processor 678 which canevaluate the response and which evaluation can then impact one orseveral models relevant to either the aggregation content or the userand the providing of one or several future content aggregations.

With reference now to FIG. 13, a flowchart illustrating one embodimentof a process 900 for automated content aggregation generation is shown.The process 900 can be performed by all or components of the contentdistribution network 100 including by, for example, one or severalservers 102. In some embodiments, these one or several servers 102 cancomprise one or several remote resources such as can occur via cloudcomputing or distributed processing. The process 900 begins at block902, wherein user identification information is received. In someembodiments, the user identification information can be received fromthe user device 106 and/or from the supervisor device 110. In someembodiments, for example, the user identification information can beprovided to the user device 106 and/or supervisor device 110 via the I/Osubsystem 526 and can be communicated to the server 102 via thecommunications subsystem 532 and the communication network 120. In someembodiments, the user identification information can comprise a user ID,password, a username, or the like.

After the user identification information has been received, the process900 proceeds to block 904 wherein content in the form of one or severaldata packets is provided to the user. In some embodiments, the one orseveral data packets can comprise text, images, video, audio, or thelike. In some embodiments, the content can be provided to the user fromthe server 102 via the communication network 120, the communicationsubsystems 532, and the user device 106. In some embodiments, thecontent provided to the user can be selected by the recommendationengine 686 and can be delivered either directly or indirectly to thepresentation module 672 and the view module 674.

After the content has been provided to the user, the process 900proceeds to block 906 wherein a content selection is received. In someembodiments, for example, the user can select all or portions of theprovided content via the I/O subsystem 526 of the user device 106. Insome embodiments, the selection can include the highlighting of text,portions of text, images, audio content, video content, or the like. Theselected content can be delivered from the I/O subsystem 526 of the userdevice 106 of the communication subsystem 532 of the user device 106 andthen, via the communication network 120, to the server 102.

After the content selection has been received, the process 900 proceedsto block 908 wherein the content selection is parsed into sentences. Insome embodiments, this parsing can be performed using one or severalNatural Language Processing (NLP) techniques and/or one or severalNatural Language Processing software programs running on, for example,the server 102. In some embodiments, the content selection can be parsedinto sentences via the identification of periods, blank spaces, capitalletters, or the like.

In some embodiments, parsing the content selection into sentences canfurther include a filtering of the selected content to achieve textcleaning. In some embodiments, the text cleaning can remove and/orreduce the number of unwanted artifacts in the selected content. In someembodiments, the text cleaning can include, for example, double quotenormalization, hyphenated word joining, the removal of footnotereferences, the removal of extra white space, or the like. In someembodiments, double quote normalization can include turning left andright double quotes into standard double quotes. In some embodiments,hyphenated word joining can include identifying words hyphenated becauseof location at the end of the line, removal of those identified hyphens,and joining the hyphenated word. In some embodiments, the footnotereferences can include the identification of footnote references and theremoval of the identified footnote references. In some embodiments, theremoval of extra white space can include the identification of two ormore consecutive whitespace characters and the replacement of those twoor more consecutive whitespace characters with a single whitespacecharacter. In some embodiments, the text cleaning can be performedbefore the selected content is parsed into sentences.

After the selected content has been parsed into sentences, the process900 proceeds to block 910 where the sentences are selected. In someembodiments, this can include the selection of a previously unselectedsentence. In some embodiments, for example, the selecting of a sentencecan include identification of the sentences identified via parsing ofthe content into sentences in block 908, the identification ofpreviously unselected sentences, and the selection of one of thepreviously unselected sentences. In some embodiments, a value indicativeof selection is associated with a sentence when that sentence isselected.

After a sentence has been selected, the process 900 proceeds to block912 wherein part of speech tags are generated for portions of theselected sentence. In some embodiments, the part of speech tags canidentify a category or classification of one or several words or phrasesin the selected speech. In some embodiments these can include, forexample, classification as a noun, pronoun, adjective, determiner, verb,adverb, preposition, conjunction, interjection, or the like. In someembodiments, these can include identification of one or several nounphrases, verb phrases, adjective phrases, adverb phrases, prepositionalphrases, or the like. In some embodiments, the parts of speech of theselected sentence can be identified using Natural Language Processingtechniques performed by the server 102 and/or Natural LanguageProcessing software executed by the server 102. In some embodiments, atag can be associated with each word and/or phrase associated with apart of speech. In some embodiments, this tag can include identificationof the associated word and/or phrase and data indicating the identifiedpart of speech. These tags can be stored within the database server 104,and can be specifically stored in the content library database 303and/or in the user profile database 301.

After the part of speech tags have been generated, the process 900proceeds to block 914 where in a parse tree is generated for theselected sentence. The parse tree is an ordered, rooted tree thatrepresents the syntactic structure of the selected sentence according toa context-free grammar. In some embodiments, the parse tree can becreated according to one or several phrase structure grammars or one orseveral dependency grammars.

In some embodiments, the parse tree can comprise a root and a pluralityof branches. In some embodiments, the root can identify the sentenceparsed to create the parse tree, and in some embodiments, the root ofthe parse tree can be identified with the letter “S”. In someembodiments, each of the plurality of branches can be associated withone of the words or phrases associated with the part of speech tag. Theparse tree can be generated using Natural Language Processing techniquesperformed by the server 102 and/or Natural Language Processing softwareexecuted by the server 102. The parse tree can be stored within thedatabase server 104, and can be specifically stored in the contentlibrary database 303 and/or in the user profile database 301.

In some embodiments, after a parse tree is created, the parse tree canbe evaluated to validate the parse tree was created for a sentence. Inthe event that the parse tree is not associated with a sentence such as,for example, a complete sentence, then the created parse tree isdiscarded. If the parse tree is discarded, then the process proceeds toblock 922 and continues as will be outlined below.

After the parse tree has been created, the process 900 proceeds to block916 wherein noun phrases in the parse tree are identified. In someembodiments, this can include the evaluation of some or all of thebranches of the parse tree to determine whether each of those, some orall of the branches of the parse tree are associated with the nounphrase. The identification of the noun phrases in the parse tree can beautomatically performed by the server 102 in response to the creation ofa parse tree for the selected sentence.

After the noun phrases in the parse tree have been identified, theprocess 900 proceeds to block 918 wherein a subset of rational phrasesis created. In some embodiments, the subset of rational phrasescomprises a portion of the identified noun phrases selected based on oneor several attributes. In some embodiments, the subset of rationalphrases can be selected based on attributes thought to improve thequality of the content aggregations. In some embodiments, the subset ofrational phrases can exclude noun phrases that have too many wordsand/or too few words. The creation of the subset of rational phrases canbe automatically performed by the server 102.

After the subset of rational phrases has been created, the process 900proceeds to block 920 wherein a content aggregation is generated. Insome embodiments, the content aggregation can be generated for each ofthe noun phrases in the subset of rational phrases. In some embodiments,the content aggregation can be generated such that a portion of the nounphrase is identified as an extraction and a portion of the noun phraseis identified as a presentation portion. In some embodiments, thepresentation portion of the content aggregation can be provided to theuser and the user can respond to the presentation portion by identifyingand/or attempting to identify the extraction. The content aggregationcan be generated by the server 102 and the content aggregation can bestored in the database server 104, and particularly in the user profiledatabase 301 and/or the content library database 303.

After the content aggregations been generated, the process proceeds todecision state 922 wherein it is determined if there is an additional,unselected sentence. In some embodiments, this can include determiningwhether any of the sentences parsed in block 908 is not associated withthe value indicative of selection. If any of the parsed sentences is notassociated with a value indicative of selection, then the process 900returns to block 910 and proceeds as outlined above.

Returning again to decision state 922, if it is determined that thereare no additional sentences, then the process 900 proceeds to block 924wherein content aggregation generation is completed. In someembodiments, this can include the generation and sending of an alert tothe user indicating the completion of the content aggregation for theselected content. In some embodiments, this can further include thepresentation of some or all of the generated content aggregations to theuser for selection, evaluation, review, or the like. In someembodiments, the completion of the generation of the content aggregationcan further include the linking of the generated content aggregations tothe one or several data packets from which they were generated.

With reference now to FIG. 14, a flowchart illustrating one embodimentof a process 940 for automated identification of noun phrases in a parsetree is shown. The process 940 can be performed as a part of, or in theplace of the step of block 916 shown in FIG. 13. In some embodiments,the process 940 can be performed by the server 102 or by anothercomponent of the content distribution network 100. The process 940begins at block 942 wherein the root of the parse tree is identified. Insome embodiments, the root of the parse tree can be identified bysearching the parse tree for the “S” indicative of the root.

After the root of the parse tree has been identified, the process 940proceeds to block 944 and advances from the root to a branch. In someembodiments, this can include identifying all of the branches in theparse tree, identifying a subset of branches that have not been advancedto, and then advancing to one of that subset of branches. In someembodiments, a value indicating that a branch has been advanced to canbe associated with a branch when that branch is advanced to. In someembodiments, this value can be stored in the same portion of thedatabase server 104 containing the parse trees such as, for example, theuser profile database 301 and/or the content library database 303.

After advancing from the root to a branch, the process 940 proceeds toblock 946 wherein the part of speech tag of the advanced to branch isretrieved. In some embodiments, this can include identifying theadvanced to branch, identifying the part of speech tag associated withthat branch, and retrieving that part of speech tag from the databaseserver 104, and specifically from the user profile database 301 and/orthe content library database 303. After the part of speech tag has beenretrieved, the process 940 proceeds to block 948 wherein the part ofspeech of the branch is identified. In some embodiments, this caninclude extracting data from the part of speech tag retrieved in block946, and specifically can include extracting data identifying the partof speech from the part of speech tag retrieved in block 946.

After the part of speech of the branch has been identified, the process940 proceeds to decision state 950 wherein it is determined if there isan additional, as yet un-advanced to branch. In some embodiments, thiscan include retrieving information identifying all the branches of theparse tree and determine whether any of the branches of the parse treeare not associated with a value indicative of having been advanced to.If some of the branches are not associated with a value indicative ofhaving been advanced to, then the process 940 returns to block 944 andproceeds as outlined above. Alternatively if all the branches areassociated with a value indicative of having been advanced to, then theprocess 940 advances to block 952 and proceeds to block 918 of FIG. 13.

With reference now to FIG. 15, a flowchart illustrating one embodimentof a process 960 for automated creation of a subset of rational phrasesis shown. The process 960 can be performed as a part of, or in the placeof the step of block 918 shown in FIG. 13. Specifically, the process 960can be performed to create a subset of rational phrases. The process 960begins at block 962 wherein a noun phrase is selected. In someembodiments, this can include the selection of a previously unselectednoun phrase. In some embodiments, for example, the selecting of a nounphrase can include identification of the noun phrase identified in theparse tree in block 916 of process 900, the identification of previouslyunselected noun phrases, and the selection of one of the previouslyunselected noun phrases. In some embodiments, a value indicative ofselection is associated with a noun phrase when that noun phrase isselected.

After the noun phrase has been selected, the process 960 proceeds toblock 964, wherein the word count in the selected noun phrase isdetermined. In some embodiments, this can include determining, based onthe parsing of the noun phrase, words contained in the noun phrase andincrementing a counter for each of the words in the noun phrase. In someembodiments, the count of words in the noun phrase can be stored in thedatabase server 104.

After the count of words in the noun phrase is determined, the process960 proceeds to block 966, wherein a size threshold is received and/orretrieved. In some embodiments, for example, the size threshold cancomprise a single value, and in some embodiments, the size threshold cancomprise a plurality of values. In some embodiments, for example, thesize threshold can specify a minimum value and a maximum value. The sizethreshold can be, in some embodiments, received from the user device106, and in some embodiments, the size threshold can be retrieved formthe database server 104.

After the size threshold has been received/retrieved, the process 960proceeds to block 968, wherein the word count is compared to the sizethreshold. In some embodiments, this can include a comparison of theword count to a single value or to a plurality of values. In embodimentsin which the threshold comprises a minimum value and a maximum value,the word count can be compared to both the minimum and maximum values.After the word count has been compared to the size threshold, theprocess 960 proceeds to block 970, wherein a Boolean value is assignedto the selected noun phrase based on the result of the comparison. Insome embodiments, for example, a first Boolean value can be assigned tothe noun phrase when the noun phrase has an acceptable size incomparison to the threshold, and a second Boolean value can be assignedto the noun phrase when the noun phrase has an unacceptable size incomparison to the threshold. In some embodiments, these Boolean valuescan be associated with their noun phrase and can be stored in thedatabase server 104 and particularly in the user profile database 301and/or the content library database 303.

After the Boolean value has been assigned, the process 960 proceeds todecision state 972, wherein it is determined if there is an additionalnoun phrase. In some embodiments, this can include determining if thereare any noun phrases that are not associated with the value indicativeof selection. If it is determined that there are additional nounphrases, then the process 960 returns to block 962 and proceeds asoutlined above. Alternatively, if it is determined that there are noadditional, previously unselected noun phrases, then the process 960continues to block 974 and proceeds to block 920 of FIG. 13.

With reference now to FIG. 16, a flowchart illustrating one embodimentof a detailed process 980 for automated content aggregation generationis shown. The process 980 can be performed as a part of, or in the placeof the step of block 920 shown in FIG. 13. Specifically, the process 980can be performed in generating the content aggregation. The process 980begins at block 982, wherein a noun phrase is selected. In someembodiments, this can include the selection of a previously unselectednoun phrase. In some embodiments, for example, the selecting of a nounphrase can include identification of the noun phrase identified in theparse tree in block 916 of process 900, the identification of previouslyunselected noun phrases, and the selection of one of the previouslyunselected noun phrases. In some embodiments, a value indicative ofselection is associated with a noun phrase when that noun phrase isselected.

After the noun phrase has been selected, the process 980 proceeds toblock 984, wherein one or several words within the noun phrase areidentified as the extraction. In some embodiments, the extraction is theportion of the content aggregation that is not initially provided to theuser, but that form the correct response to a presentation portion ofthe content aggregation. In some embodiments, the extraction portion canbe selected by, for example, random selection from one or several of thewords of the selected noun phrase. In some embodiments, this randomselection can be made using a random number generator. In someembodiments, the extraction portion can be selected from one or severalclasses of words or phrases within the selected noun phrase. In someembodiments, for example, the extraction portion can be selected fromone or several nouns, verbs, adjectives, adverbs, cardinal numbers, orthe like. In some embodiments, the words or phrases within the selectednoun phrase that can be chosen as the extraction portion are identifiedand the extraction portion is selected from those words or phrases. Insome embodiments, the extraction portion can be identified by the server102 and the extraction portion can be stored in the database server 104and specifically in the user profile database 301 and/or the contentlibrary database 303.

After one or several words or phrases have been designated as theextraction, the process 980 proceeds to block 986 wherein a presentationportion is generated. In some embodiments, the presentation portioncomprises a portion of the content aggregation that is initiallydisplayed and/or provided to the user. In some embodiments, thepresentation portion can be associated with a prompt to the user toprovide a response to the presentation portion. In some embodiments, adesired and/or correct response to the presentation portion comprises oris the extraction portion.

In some embodiments, the generation of the presentation portion cancomprise an identification of all the words in the noun phrase, theelimination of the extraction portion from all the words in the nounphrase and the insertion of placeholder markings such as, for example,underlining in the location within the noun phrase from which theextraction portion was removed. In some embodiments, presentationportion can be automatically generated by the server after thedesignation of the extraction portion.

After the presentation portion has been generated, the process 980proceeds to block 988 wherein one or several content tags associatedwith the selected noun phrase are generated. In some embodiments, theseone or several content tags can link the selected noun phrase to thedata packet from which it was taken and/or to one or several topics orskills associated with the data packet from which the selected nounphrase was taken. In such an embodiment, the generation of the contenttag can comprise identifying the relevant data packet and/or topics orskills within the relevant data packet and associating an indicator ofthe relevant data packet and/or topics or skills within the relevantdata packet with the selected noun phrase.

In some embodiments, the one or several content tags can be generatedbased on computer analysis of the selected noun phrase. In someembodiments, this can comprise semantic analysis of all or portions ofthe selected noun phrase. In such an embodiment, server 102 can analyzethe selected noun phrase and/or one or several words in the selectednoun phrase and search the syllabus or schedule for tasks, topics,skills, and/or skill levels associated with the selected noun phraseand/or one or several words in the selected noun phrase. When tasks,topics, skills, and/or skill levels are identified, a tag comprisingdata identifying those tasks, topics, skills, and/or skill levels can becreated and associated with the selected noun phrase and/or with one orboth of the generated extraction portion and the generated presentationportion.

After the content tag has been generated, the process 980 proceeds toblock 990 wherein the generated portions are combined. In someembodiments, this can include the linking and/or combination of theextraction portion, the presentation portion, and/or the content taginto a single content aggregation. In some embodiments, the contentaggregation can be further combined with the selected noun phrase and/ora pointer to the selected noun phrase, and in other embodiments, theportions are combined to form a content aggregation independent andseparate from the selected noun phrase.

After the portions are combined, the process 980 proceeds to block 992wherein the content aggregation is stored. In some embodiments, thecontent aggregation can be stored in the database server 104 andspecifically in, for example, the user profile database 301 and/or thecontent library database 303. In some embodiments, the contentaggregation can be stored in multiple databases within the databaseserver such as a specific user accessible database for the user creatorof the content aggregation and a generally accessible database for userswho did not create, or do not participate in creation of the contentaggregation. In some embodiments, users may have different access rightsbased on the location of the content aggregation and/or users may havedifferent access rights based on whether they were the creator of thecontent aggregation. In some embodiments, for example, a user creator ofa content aggregation may have access rights to edit and/or modify thecontent aggregation whereas a non-creator user of a content aggregationmay only have viewing rights to that same content aggregation.

After the content aggregation has been stored, the process 980 proceedsto block 994 wherein it is determined if there are additional phrases inthe subset of rational phrases. In some embodiments, this can includedetermining if there are any phrases in the subset of rational phrasesthat are not associated with the indicator of selection. If there areany additional phrases, then the process 980 returns to block 982 andproceeds as outlined above. If there are no additional phrases, then theprocess 980 continues to block 996 and proceeds to block 922 of FIG. 13.

With reference now to FIG. 17, a flowchart illustrating one embodimentof a process 1000 for automated content aggregation evaluation is shown.The process 1000 can be performed by all of the components of thecontent distribution network 100 including by, for example, one orseveral servers 102. In some embodiments, these one or several servers102 can comprise one or several remote resources such as can occur viacloud computing or distributed processing. The process 1000 begins atblock 1002, wherein a content aggregation is retrieved. In someembodiments, the content aggregation can be the content aggregationgenerated according to the process of FIG. 13. The content aggregationcan be retrieved from the database server and particularly from the userprofile database 301 and/or the content library database 303.

After the content aggregation has been retrieved, the process 1000proceeds to block 1004 wherein a content aggregation score is generatedby, for example, the server 102. In some embodiments the source can begenerated based on one or several attributes of the content aggregationsuch as, for example, the number of words in the content aggregation,the inclusion of one or several proper names in the content aggregation,the inclusion of one or several numbers in a content aggregation, or thelike. In some embodiments, this can include, for example, identifyingfeatures of the content aggregation such as proper names and/or numbersin the extraction portion of the content aggregation, and determiningattributes of those identified features such as a number of proper namesand/or numbers in the extraction portion of the content aggregation. Insome embodiments, a name counter can be incremented once for each propername in the extraction portion and a number counter can be incrementedonce for each number in the extraction portion. In some embodiments, theattributes of the identified features, such as the number of propernames and/or numbers in the extraction portion, can be input into ascoring algorithm, which scoring algorithm can generate the contentaggregation score. In some embodiments, for example, a contentaggregation such as a digital flash card can have an increased scorewhen one or several proper names are the extraction portion and/or whenthe extraction portion includes one or several numbers.

After the content aggregations score has been generated, the process1000 proceeds block 1006 wherein a score threshold is retrieved. In someembodiments, the score threshold can delineate between contentaggregations having a satisfactory score and content aggregations havingan unsatisfactory score. In some embodiments, the score threshold cancomprise a single value, and in some embodiments the score threshold cancomprise a plurality of values. The score threshold can be retrievedfrom the database server 104. After the score threshold has beenretrieved, the process 1000 proceeds to block 1008 wherein the scoregenerated in block 1004 is compared to the score threshold retrieved inblock 1008. In some embodiments, this can include determining whetherthe score generated in block 1004 is greater than, less than, or equalto the score threshold.

After the score threshold has been compared to the generated score, theprocess 1000 proceeds to decision state 1010 wherein it is determinedwhether to keep the content aggregation. In some embodiments, this caninclude determining whether the comparison of the score threshold to thegenerated score indicates that content threshold is associated with anacceptable score. If it is determined to not keep the contentaggregation, then the process 1000 proceeds to block 1012 wherein thecontent aggregation is discarded. In some embodiments, the discarding ofthe content aggregation can result in the deletion of the contentaggregation and/or any data or files associated with the contentaggregation from the database server 104.

Returning again to decision state 1010, if it is determined to keep thecontent aggregation, then the process 1000 proceeds block 1014 whereinthe evaluation model is retrieved. In some embodiments, the evaluationmodel can be a statistical model configured for evaluating one orseveral content aggregations. In some embodiments, the statistical modelcan operate based on inputs comprising one or several content featuresand/or content parameters. As used herein, a content feature issomething directly observable from the content of a content aggregationsuch as, for example, a count of, or an indicator of the presence orabsence of one or several words, word types, phrases, or the like. Asused herein, a content parameter is one or several values calculatedbased on one or several content features. Content parameters caninclude, for example, ratios, averages, or the like. In someembodiments, the evaluation model can be trained based on past orpreviously collected data relating to content aggregations andperformances of users having used those content aggregations. Theevaluation model can be retrieved from the database server 104, andspecifically from the model data store 309.

After the evaluation model has been retrieved, the process 1000 proceedsto block 1016 wherein the content aggregation is parsed. In someembodiments, the content aggregation can be parsed using one or severalNatural Language Processing (NLP) techniques and/or one or severalNatural Language Processing software programs running on, for example,the server 102. In some embodiments, the content selection can be parsedinto words, phrases, parts of speech, or the like. After the contentaggregation has been parsed, the process 1000 proceeds to block 1018wherein one or several content features are extracted from the parsedcontent aggregation. In some embodiments, these content features cancomprise a count of the number of words in the content aggregation, acount of the number of words in the presentation portion of the contentaggregation, a count of the number of words in the extraction portion ofthe content aggregation, a count of the number of characters in theextraction portion and/or the presentation portion, a count of thenumber of letters, digits, and/or numbers in the extraction portionand/or the presentation portion, the parts of speech of the words in theextraction portion, a number of capitalized letters in the extractionportion and/or the presentation portion, or the like. In someembodiments, the content features can be extracted from the parsedcontent aggregation by the incrementing of one or several counters basedon the results of the parsing of the content aggregation by, forexample, the server 102.

After the content features have been extracted, the process 1000proceeds to block 1020 wherein one or several content parameters aregenerated. In some embodiments, these content parameters can begenerated from the content features. These content parameters caninclude, for example, a ratio of the number of words in the extractionportion to the number of words in the presentation portion of thecontent aggregation, a ratio of the number of characters, digits, and/orletters in the extraction portion to the number of characters, digits,and/or letters in the presentation portion, an average vector spacerepresentation of blank words such as can be constructed using wordembedding techniques such as Word2vec or other similar models, or thelike. The content parameters can be generated by, in some embodiments,the server 102.

After the content parameters have been generated, the process 1000proceeds to block 1022 wherein some or all of the content featuresand/or content parameters are input into the evaluation model. After thecontent features and/or content parameters are input into the evaluationmodel, the process 1000 proceeds to block 1024 wherein an evaluationresult is generated as the output of the evaluation model. In someembodiments, the output result can be generated by the server 102.

After the evaluation result has been generated, the process 1000proceeds to block 1026 wherein the evaluation result is compared to anevaluation threshold. In some embodiments, the evaluation threshold cancomprise one or several values delineating between acceptable andunacceptable content aggregations based on the evaluation result. Theevaluation threshold can be stored in the database server 104, andparticularly in the threshold database 310. In some embodiments, thecomparison of the evaluation result to the evaluation threshold canfurther include the assigning of a Boolean value to the contentaggregation based on the result of the comparison. In some embodiments,for example, a first Boolean value can be assigned to the contentaggregation when the content aggregation has an acceptable evaluationresult in comparison to the threshold, and a second Boolean value can beassigned to the content aggregation when the content aggregation has anunacceptable evaluation result in comparison to the threshold. In someembodiments, these Boolean values can be associated with their contentaggregation and can be stored in the database server 104 andparticularly in the user profile database 301 and/or the content librarydatabase 303.

After the evaluation result has been compared to the evaluationthreshold, the process 1000 can proceed to block 1028 wherein thecontent aggregation is stored or discarded based on the comparisonresult and/or based on the Boolean value associated with that contentaggregation. In some embodiments, the storing or discarding can beperformed by the server 102. In the event that a content aggregation isstored, the content aggregation can be stored in the database server104, and specifically in the user profile database 301 and/or thecontent library database 303. In the event that the content aggregationis discarded, the discarding of the content aggregation can result inthe deletion of the content aggregation and/or any data or filesassociated with the content aggregation from the database server 104.

In some embodiments, and as part of the storing or discarding of thecontent aggregation based on the comparison of the evaluation result andthe evaluation threshold, one or several electrical signals can begenerated by the server 102 and can be sent to the user device 106and/or supervisor device 110 via, for example, the communication network120 and the communication subsystem 532. In some embodiments, theseelectrical signals can be sent in the form of a communication such as analert as discussed above.

With reference now to FIG. 18, a flowchart illustrating one embodimentof a process 1100 for automated evaluation of a set of contentaggregations is shown. The process 1100 can be performed by all of thecomponents of the content distribution network 100 including by, forexample, one or several servers 102. In some embodiments, these one orseveral servers 102 can comprise one or several remote resources such ascan occur via cloud computing or distributed processing. The process1100 begins at block 1102 wherein a user identifier or useridentification data is received and/or retrieved. In some embodiments,the user identifier can further include a request for evaluation of aset of content aggregations and one or several identifiers specifyingthe content aggregations comprising that set of content aggregations. Insome embodiments, the user identifier can be received from the userdevice 106 and/or from the supervisor device 110. In some embodiments,for example, the user identifier can be provided to the user device 106and/or supervisor device 110 via the I/O subsystem 526 and can becommunicated to the server 102 via the communications subsystem 532 andthe communication network 120. In some embodiments, the user identifiercan comprise a user ID, password, a username, or the like.

After the user identification data has been received, the process 1100proceeds to block 1104 wherein the user is identified. In someembodiments, this can include a comparison of the user identificationdata stored in the user profile database 301 and the user identifier oruser identification data received in block 1102. After the user has beenidentified, the process 1100 proceeds to block 1106 wherein objectnetwork data is received and/or retrieved. In some embodiments, the stepof block 1106 can comprise receiving data indicative of one or severaldata packets, tasks, skills, or skill levels, or the like in bodies inthe object network, a syllabus, schedule, or the like. In someembodiments, the object network data can be received and/or retrievedfrom the database server 104 and specifically from one of the databasesof the database server 104 storing the object network such as, forexample, the content library database 303. In some embodiments, theobject network data can characterize: the data packets in the objectnetwork; the tasks, skills, and/or skill levels associated with datapackets in the object network; or the like.

After the object network data has been retrieved, the process 1100proceeds to block 1108 wherein user aggregation data is retrieved. Insome embodiments, user aggregation data can comprise data relating tothe content aggregations in the user's specific database of contentaggregations. In some embodiments, this aggregation data can beretrieved from the database server 104 and specifically from the contentlibrary database 303 and/or the user profile database 301.

After the user aggregation data has been retrieved, the process 1100proceeds to block 1114 wherein the object network data and the useraggregation data are compared. In some embodiments, this comparison caninclude a determination of whether all of the data packets, tasks,skills, skill levels, or the like identified in the object network dataare associated with content aggregations identified in the useraggregation data. In some embodiments, a first Boolean value can beassociated with each data packet, task, skill, skill level, or the likethat is not associated with one or several content aggregationsidentified in the user aggregation data and a second Boolean value canbe associated with each data packet, task, skill, skill level, or thelike that is associated with one or several content aggregationsidentified in the user aggregation data.

After the object network data and the user aggregation data have beencompared, the process 1100 proceeds to block 1116 wherein an aggregationgap is identified. In some embodiments, the identification of theaggregation gaps can comprise the identification of one or several datapackets, tasks, skills, skill levels, or the like that are notassociated with a content aggregation identified in the user aggregationdata or that are not associated with a sufficient number of contentaggregations identified in the user aggregation data. In someembodiments, this determination can be made by identifying the one orseveral data packets, tasks, skills, skill levels, or the like that areassociated with the first Boolean value.

After the aggregation gaps have been identified, the process 1100proceeds to block 1118 wherein a gap alert is generated and sent. Insome embodiments this can comprise one or several communications in theform of one or several electrical signals that identify the presence orabsence of one or several aggregation gaps. In some embodiments, thesecommunications can be generated by the server 102 and can be sent to theuser device 106 and/or the supervisor device 110 as discussed above withrespect to alerts.

After the generation and sending of the gap alert, the process 1100proceeds to block 1120 wherein one or several potential aggregation gapresolutions are identified. In some embodiments, this can include, forexample a searching of databases unrelated to the specific user for oneor several content aggregations that could eliminate the identifiedaggregation gap and/or identify one or several data packets forproviding to the user in connection with the prompt for the generationof additional content aggregations. In some embodiments, the server 102can be configured to automatically review one or several other contentaggregation databases for content aggregations to resolve any identifiedaggregation gaps.

After the potential aggregation gap resolutions have been identified,the process 1100 proceeds to block 1122 wherein one or severalaggregation gaps are resolved. In some embodiments, this can include theinclusion of any identified content aggregations into the database ofcontent aggregations associated with the specific user and/or theupdating of the database of content aggregations associated with aspecific user to include any newly generated content aggregations. Afterthe aggregation gaps have been resolved, the process 1100 proceeds toblock 1124 wherein a resolution report is generated and sent. In someembodiments, the resolution report can comprise a communicationcomprising electrical signals indicating that some or all of theidentified potential aggregation gaps have been resolved and/oridentifying the specific resolution of those identified potentialaggregation gaps. In some embodiments, this communication can comprisean alert generated and sent as discussed above with respect to otheralerts.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application-specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine-readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein, the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read-only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system comprising: a database storing: aplurality of digital flash cards each comprising a prompt and a responseto the prompt; a flash card score threshold used to determine whether todiscard or further analyze a digital flash card in the plurality ofdigital flash cards; an evaluation score threshold used to determinewhether to discard or store the flashcard in the database; and trainingdata comprising a plurality of performance results from a plurality ofusers interacting with the plurality of digital flash cards; a servercomprising a computing device coupled to a network and including atleast one processor executing instructions within a memory which, whenexecuted, cause the system to: train a statistical model using thetraining data; select the digital flash card from the database; parse acontent of the digital flash card to identify at least one contentfeature of the digital flash card; calculate a digital flash card scorebased on the at least one content feature; responsive to a determinationthat the digital flash card score is above the flash card scorethreshold: input the at least one feature as at least one contentparameter into the statistical model; receive, in response to thestatistical model processing the at least one content parameter, anevaluation result; and responsive to a determination that the evaluationresult is greater than the evaluation score threshold, store the digitalflash card in the database.
 2. The system of claim 1, the instructionsfurther causing the system, responsive to a determination that thedigital flash card score is below the digital flash card threshold, todelete the digital flash card from the database.
 3. The system of claim1, the instructions further causing the system, responsive to adetermination that the evaluation result is below the evaluation scorethreshold, to delete the digital flash card from the database.
 4. Thesystem of claim 1, further comprising a Natural Language Processingsoftware running on the server and configured to parse the content ofthe digital flash card and apply other text analytics operations to thecontent of the digital flash card.
 5. The system of claim 1, theinstructions executing a digital flash card scoring algorithm within theinstructions that cause the system to calculate the digital flash cardscore according to: a number of words in the content of the flash card;the inclusion of at least one proper name in the content of the flashcard; and the inclusion of at least one number in the flash card.
 6. Thesystem of claim 5, the instructions further causing the system toincrement a counter to determine the number of words in the content ofthe flash card.
 7. The system of claim 1, the instructions furthercausing the system to identify the at least one content featureaccording to a presence or an absence of, or a count of, characters,digits, words, word types, phrases, parts of speech, or capital lettersin the flash card.
 8. The system of claim 1, the instructions furthercausing the system to identify the at least one content parameteraccording to a ratio or an average of characters, digits, words, wordtypes, phrases, parts of speech, or capital letters within the promptand the response to the prompt.
 9. A system comprising: a servercomprising a computing device coupled to a network and including atleast one processor executing instructions within a memory which, whenexecuted, cause the system to: store, in a database: a plurality ofdigital flash cards each comprising a prompt and a response to theprompt; a flash card score threshold used to determine whether todiscard or further analyze a digital flash card in the plurality ofdigital flash cards; an evaluation score threshold used to determinewhether to discard or store the flashcard in the database; and trainingdata comprising a plurality of performance results from a plurality ofusers interacting with the plurality of digital flash cards; select thedigital flash card from the database; parse a content of the digitalflash card to identify at least one content feature of the digital flashcard; calculate a digital flash card score based on the at least onecontent feature; responsive to a determination that the digital flashcard score is above the flash card score threshold: input the at leastone feature as at least one content parameter into a statistical model;receive, in response to the statistical model processing the at leastone content parameter, an evaluation result; and responsive to adetermination that the evaluation result is greater than the evaluationscore threshold, store the digital flash card in the database.
 10. Thesystem of claim 9, the instructions further causing the system,responsive to a determination that the digital flash card score is belowthe digital flash card threshold, to delete the digital flash card fromthe database.
 11. The system of claim 9, the instructions furthercausing the system, responsive to a determination that the evaluationresult is below the evaluation score threshold, to delete the digitalflash card from the database.
 12. The system of claim 9, furthercomprising a Natural Language Processing software running on the serverand configured to parse the content of the digital flash card and applyother text analytics operations to the content of the digital flashcard.
 13. The system of claim 9, the instructions executing a digitalflash card scoring algorithm within the instructions that cause thesystem to calculate the digital flash card score according to: a numberof words in the content of the flash card; the inclusion of at least oneproper name in the content of the flash card; and the inclusion of atleast one number in the flash card.
 14. A method comprising the stepsof: storing, by a server coupled to a network and including a processorexecuting instructions within a memory, within a database coupled to anetwork: a plurality of digital flash cards each comprising a prompt anda response to the prompt; a flash card score threshold used to determinewhether to discard or further analyze a digital flashcard in theplurality of digital flashcards; an evaluation score threshold used todetermine whether to discard or store the flashcard in the database; andtraining data, comprising a plurality of performance results from aplurality of users interacting with the plurality of digital flashcards; selecting, by the server, the digital flash card from thedatabase; parsing, by the server, a content of the digital flash card toidentify at least one content feature of the digital flash card;calculating, by the server, a flash card score based on the at least onecontent feature; responsive to a determination that the flash card scoreis above the flash card score threshold: inputting, by the server, theat least one feature as at least one content parameter into astatistical model; receiving, by the server in response to thestatistical model processing the at least one content parameter, anevaluation result; and responsive to a determination that the evaluationresult is greater than the evaluation score threshold, storing, by theserver, the digital flash card in the database.
 15. The method of claim14, further comprising the step of, responsive to a determination thatthe digital flash card score is below the digital flash card threshold,deleting, by the server, the digital flash card from the database. 16.The method of claim 14, further comprising the step of, responsive to adetermination that the evaluation result is below the evaluation scorethreshold, deleting, by the server, the digital flash card from thedatabase.
 17. The method of claim 14, further comprising the step ofparsing, by a Natural Language Processing software running on theserver, the content of the digital flash card.
 18. The method of claim14, further comprising the steps of: calculating, the a number of wordsin the content of the flash card according to a digital flash cardscoring algorithm within the instructions; identifying the inclusion ofat least one proper name in the content of the flash card according tothe digital flash card scoring algorithm; and identifying the inclusionof at least one number in the flash card according to the digital flashcard scoring algorithm.
 19. The method of claim 14, further comprisingthe step of identifying, by the server, the at least one content featureaccording to a presence or an absence of, or a count of, characters,digits, words, word types, phrases, parts of speech, or capital lettersin the flash card.
 20. The method of claim 14, further comprising thestep of identifying, by the server, the at least one content parameteraccording to a ratio or an average of characters, digits, words, wordtypes, phrases, parts of speech, or capital letters within the promptand the response to the prompt.